Multidimensional GIS for satellite imagery analysis

• Autorzy: Drypczewski K. , Markiewicz Ł. , Stepnowski A.
• Języki publikacji: EN

Abstrakty

EN

Multidimensional Geographical Information System allows storing, querying and processing of multidimensional query data. It is able to process satellite imagery and provide tools for its analysis. In the article authors present the developed system that analyzes a time series of SENTINEL - 1 mission satellite imagery acquired over the coast of Poland. The algorithm used finds and detects changes in the shape of the coastline over a long period of time. The system uses a Raster Data Manager array database management system to simplify the process of data querying, trimming, storing and analysing. Authors present how the recent trends in GIS development, like RASDAMAN, can be applied to satellite imagery processing.

Słowa kluczowe

EN

Multidimensional GIS; Raster Data Manager; RASDAMAN; SENTINEL-1; SAR; Coastal Monitoring; coast change detection

• Wydawca: Polskie Towarzystwo Akustyczne. Oddział Gdański
• Czasopismo: Hydroacoustics
• Rocznik: 2017
• Tom: Vol. 20
• Strony: 41--50
• Opis fizyczny: Bibliogr. 19 poz., rys.

Twórcy

autor

Drypczewski K.

• Faculty of Electronics, Telecommunications and Informatics Gdansk University of Technology Gabriela Narutowicza 11/12, 80-233 Gdańsk

autor

Markiewicz Ł.

• Faculty of Electronics, Telecommunications and Informatics Gdansk University of Technology Gabriela Narutowicza 11/12, 80-233 Gdańsk

autor

Stepnowski A.

• Faculty of Electronics, Telecommunications and Informatics Gdansk University of Technology Gabriela Narutowicza 11/12, 80-233 Gdańsk

Bibliografia

• [1] K. Drypczewski, A. Stepnowski, SOLAP GIS in maritime research, Hydroacoustics pp. 93-100, vol. 19, 2016, Gdansk.
• [2] K. Drypczewski, B. Wiśniewski, M. Kulawiak, K. Bruniecki, GIS for processing multidimensional marine data in SAAS model, Hydroacoustics pp. 47-58, vol. 16, 2013, Gdansk.
• [3] P. Snoeij, E.Attema, M.Davidson, The Sentinel-1 radar mission: status and performance, Radar Conference - Surveillance for a Safer World, 2009.
• [4] A. Rucci, A. Ferretti, A. Monti Guarnieri, F. Rocca, Sentinel 1 SAR interferometry applications: The outlook for sub millimeter measurements, Remote Sensing of Environment Volume 120, p. 156-163, 2012.
• [5] S-1 Polarimetry, https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/productoverview/polarimetry.
• [6] P. Baumann, A. Dehmel, M. Höfner, N. Widmann, Exploring Sensor and Simulation Raw Data with RasDaMan. Extending Database Technology (EDBT) 2000, March 27- 31, 2000, Konstanz, Germany.
• [7] L. Libkin, R. Machlin, L. Wong, A query language for multidimensional arrays: Design, implementation, and optimization techniques, Proceedings of the ACM SIGMOD'96, 1996, Montreal, Canada, pp. 228 - 239.
• [8] A. Marathe, K. Salem: Query Processing Techniques for Arrays, Proceedings of the ACM SIGMOD'99, 1999, Philadelphia, USA, pp. 323-334.
• [9] S. Sarawagi, M. Stonebraker: Efficient Organization of Large Multidimensional Arrays, Proceedings of the ICDE’94, 1994 Houston, USA, pp. 328-336.
• [10] P. Baumann, A. Dehmel, P. Furtado, R. Ritsch, N. Widmann, Spatio-Temporal Retrieval with RasDaMan. Very Large Data Bases (VLDB) 1999, September 7-10, 1999, Edinburgh, Scotland, UK, Morgan Kaufmann 1999, pp. 746-749.
• [11] Rasdaman website, http://www.rasdaman.org/
• [12] P. Baumann, Query Language Guide, http:// rasdaman.eecs.jacobsuniversity.de/trac/rasdaman/browser/manuals_and_examples/manuals/pdf/ql-guide.pdf.
• [13] International Organization for Standardization (ISO): Database Language SQL. ISO 9075, 1992(E), 1992.
• [14] Copernicus overview, http://www.esa.int/Our_Activities/Observing_the_Earth/Copernicus/Overview4
• [15] S-1 acquisition, https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1- sar/acquisition-modes.
• [16] Copernicus Open Access Hub, https://scihub.copernicus.eu/.
• [17] M. Piccardi, Background subtraction techniques: a review, IEEE International Conference on Systems, Man and Cybernetics, 2004.
• [18] Dar-Shyang Lee, Effective Gaussian mixture learning for video background subtraction, IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 27, Issue 5, 2005.
• [19] O.Barnich, M. Van Droogenbroeck, ViBe: A Universal Background Subtraction Algorithm for Video Sequences, IEEE Transactions on Image Processing, Volume 20, Issue 6, 2011.

Uwagi

PL

Opracowanie w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).